- % loss ?? ? % loss of output of output of output 2005 40,083 0.211 27.737 0.233 25.924 0.903 1.813 2006 33,742 0.229 26.452 0.254 24.593 0.902 1.859 2007 37,220 0.240 25.740 0.266 23.879 0.902 1.860 2008 39,365 0.241 25.685 0.266 23.857 0.904 1.828 2009 37,814 0.193 29.044 0.217 26.957 0.888 2.087 2010 38,123 0.207 27.969 0.232 25.983 0.894 1.986 2011 37,561 0.205 28.137 0.232 25.921 0.883 2.216 2012 37,644 0.209 27.834 0.236 25.677 0.886 2.157 2013 36,605 0.207 27.987 0.234 25.845 0.886 2.142 Average 37,573 0.216 27.398 0.241 25.404 0.894 1.994 Notes: This table uses the ABI/ABS sample. ?? is the estimate of aggregate credit market frictions derived in Equation (18). Output loss is the proportionate fall in output as a result of credit frictions calculated using Equation (20). ?? ? is the scale component defined in Equation (22) and ?? ?
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- parable to that of Riley et al (2015). The sectors we drop are those where output is hard to measure, namely: financial services, non-market service sectors (e.g. education, health, social work and the public sector), agriculture, mining and quarrying, utilities, real estate, and non-profit organizations.
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- The actual default rates used by PD Model are averages by risk category which incorporate a long history of observed defaults up to the present (S&P’s CreditPro). Since we are interested in the historical perception of default probabilities, i.e. the probability of default as perceived at the time of the financial accounts data, we only use past (and not future) aggregate default information. For example, to calculate the expected one year ahead default probability in 2006 we use the 2006 risk scores combined with aggregate default rates from 1980 through 2006 and no data from 2007 or after.
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- The productivity decline was accompanied by a fall in business investment that was significantly larger than in previous recessions (Benito et al, 2010).
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- The UK Data Service was able to match 2,372,326 CRNs to an entref, representing 12,282,574 entref-year observations. From these, we drop observations with consolidation code C2 (consolidated accounts of a companyheadquarter that also has unconsolidated accounts) to avoid double counting as in Kalemli-Ozcan et al (2015) (81,354 matches). Although the majority of CRNs are matched to a single entref (and hence risk score), some CRNs are matched to the same enterprise reference number. This creates 463,726 entref-year duplicates. For these entrefs, we compute a weighted average risk score using weights based on the total assets of the individual CRNs that belong to the entref. Duplicate groups with insufficient data on total assets to calculate the weights are dropped entirely. We also sum up accounting items (e.g. total assets) at the entref level for those entrefs with multiple CRNs.
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- We focus on the market sector which includes an average of 1.5 million firms and around 16 million workers per year (see Table 1). It includes the following sectors in the ABI (SIC 1992 or 2003 sections): D Manufacturing, F Construction, G Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods, H Hotels and restaurants, I Transport, storage and communication, and K real estate, renting and business activities (but excluding real estate SIC 2-digit 70). Sectors we cover in the ABS (SIC 2007 sections) are: C Manufacturing, F Construction, G Wholesale and retail trade; repair of motor vehicles and motorcycles, H Transport and storage, I Accommodation and food service activities, J Information and communication , M Professional, Scientific and Technical Activities, N Administrative and Support Service Activities. Our sectoral coverage is com40 A small number of large enterprises with a more complex structure have several reporting units.
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Zwick, Eric and James Mahon, (2017) “Tax Policy and Heterogeneous Investment Behavior,” American Economic Review, 107(1) 217—48. 1 Figure 1: Aggregate probability of default (in %) in the market sector, broken down by firm size Notes: A firm’s default probability is given by the probability that it will default on its payments at the one-year horizon according to S&P’s PD Model and historical default rates from S&P’s CreditPro. SMEs (solid line) are Small and Medium-Sized Enterprises defined as firms with fewer than 250 employees.